What Data for This Category Looks Like
The dairy industry category’s data primarily comes from production supply chain systems, e-commerce sales platforms, offline store sales tracking systems, and user review channels. Core product attribute data such as production batch number, shelf life, and fat content updates daily. E-commerce sales and user review data syncs in real time. A single data record includes fields for SKU code, full product name, nutrient composition parameters, pricing, inventory status, user review text, and purchase channel. Most field units are g/100ml, day, yuan, and entry. Some nutrient parameters require decimal precision.
Constraints Imposed on Tool Calling and Plugins
Differing update rhythms across multi-source dairy industry data require tool calling to distinguish between synchronous and asynchronous trigger logic. Real-time data such as e-commerce sales and user reviews needs short-cycle polling or event callback mechanisms. Non-real-time production data can use scheduled triggers. SKU code and nutrient parameter field precision requirements demand clear field mapping rules in plugin parameters to avoid numerical truncation or unit mismatches. User review text varies widely in length. Content truncation rules must be configured during tool calling to adapt to knowledge base retrieval context limits. Multi-channel data sources require different interface authentication parameters via plugins to separate call permissions for supply chain, e-commerce, and store systems.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
plugin_api_timeout | 300 seconds | The response latency of internal dairy industry supply chain and e-commerce interfaces is mostly within 3 minutes. This configuration balances timeout blocking and interface fault tolerance |
plugin_field_mapping_rule | Map in the order of SKU code, nutrient parameters, and review text | Core data fields for dairy products are fixed. Mapping according to business logic avoids field misalignment or unit mismatches |
plugin_sync_trigger_interval | 300 seconds | Non-real-time production data is polled at fixed intervals. This balances system resource usage and data timeliness |
plugin_context_truncate_length | 800–1200 characters | The length of dairy product user reviews and product description text varies widely. This range adapts to most knowledge base context limits |
plugin_request_retry_count | 2 times | Multi-source interfaces for dairy products occasionally experience network fluctuations. Limited retries reduce call failure rates |
workflow_subcall_max_depth | 2 levels | Avoids overly deep nested calls that cause workflow execution timeouts. Adapts to the typical call hierarchy for dairy industry marketing content generation |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- The symptom is that after workflow A calls workflow B, only the third-party interface call step runs, and subsequent reply generation or code execution steps do not complete. The root cause is that the
response_modeparameter is not configured in workflow B, or the return value passing rule for the sub-workflow is not specified. - The symptom is that an
Error: write EPROTerror appears when calling a third-party dairy product data interface. The root cause is that the plugin’s network proxy parameter is not configured, or the interface request protocol type is not set correctly, leading to SSL handshake failure. - The symptom is that numerical truncation or unit mismatch issues occur for dairy product nutrient parameters returned by the plugin. The root cause is that the precision rule for
plugin_field_mapping_ruleis not configured, resulting in lost decimal places or confused units during field mapping.
How to Verify Configuration Completion
- Initiate a tool call test for a single SKU data record, and verify that returned fields match the preset mapping rules.
- Configure a trigger task, view workflow execution logs, and confirm that the call interval matches the preset configuration.
- Simulate long text input, and check whether the plugin truncates content according to preset rules.
- Call a nested workflow, and confirm that the parent workflow can properly read the return value after the sub-workflow completes execution.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.